Uplift Modeling
From Causal Inference to Personalization — Tutorial
CIKM 2023 Tutorial
By Felipe Moraes, Hugo Manuel Proenca, Javier Albert and Dima Goldenberg
Web Conference 2021 (WWW’21) Tutorial
By Irene Teinemaa, Javier Albert and Dima Goldenberg
Uplift modeling is a collection of machine learning techniques for estimating causal effects of a treatment at the individual or subgroup levels. Over the last years, causality and uplift modeling have become key trends in personalization at online e-commerce platforms, enabling to select the best treatment for each user in order to maximize the target business metric. Uplift modeling can be particularly useful for personalized promotional campaigns, where the potential benefit caused by a promotion needs to be weighed against the potential costs.
In this tutorial we will cover basic concepts of causality and introduce the audience to state-of-the-art techniques in uplift modeling. We will discuss the advantages and the limitations of different approaches and dive into the unique setup of constrained uplift modeling. Finally, we will present real-life applications at Booking.com and other industry leaders, and discuss challenges in implementing these models in production.
Tutorial Outline
The tutorial introduces key concepts on causality as well as recent advances in uplift modeling. The outline of the tutorial is as follows: First, we introduce basic concepts in causal inference under the Potential Outcomes framework. We continue with an overview of state-of-the-art uplift modeling techniques for evaluating and estimating conditional average treatment effects. Next, we discuss constrained uplift problems, a recent addition to the uplift modeling literature aimed at enabling cost-aware personalized treatment assignment. Lastly, we present real-life applications of uplift modeling and discuss challenges in implementing these models in production. The total duration of the tutorial is three hours.
CIKM 2023 Slides
CIKM 2023 Recording
Webconf 2021 (WWW’21) Slides
UpliftML — A Python Package for Scalable Uplift Modeling
Following up the extensive use of uplift modeling on big dataset, we released a publicly available open-source package named UpliftML.
UpliftML is a Python package for scalable unconstrained and constrained uplift modeling from experimental data. To accommodate working with big data, the package uses PySpark and H2O models as base learners for the uplift models. Evaluation functions expect a PySpark dataframe as input.
